Additional file 3: Table S3. of Peripheral blood gene expression signatures which reflect smoking and aspirin exposure are associated with cardiovascular events

This file contains Site, PI, and IRB number information from the PREDICT trial. (XLSX 10 kb

Additional file 1: Table S2. of Peripheral blood gene expression signatures which reflect smoking and aspirin exposure are associated with cardiovascular events

This file contains the RT-qPCR probe and primer sequences for the 5 genes comprising the sGES. (XLSX 38 kb

Additional file 2: Table S1. of Peripheral blood gene expression signatures which reflect smoking and aspirin exposure are associated with cardiovascular events

This file contains the primary gene expression data including the sGES (SMOKSCO), ITGA2B gene expression levels (ITGA2BPR), current aspirin use (Aspirin), current smoking status (CurrrentSmoker), whether the subject experienced MACE (Event), age in 5 year bins (AgeBin), platelet count (PLATELET), whether the subject had obstructive CAD (oCAD), and the expression levels for the individual genes comprising the smoking GES. 1 = positive, 0 = negative in all binary fields. (XLSX 192 kb

An electronic health record based model predicts statin adherence, LDL cholesterol, and cardiovascular disease in the United States Military Health System

<div><p>HMG-CoA reductase inhibitors (or “statins”) are important and commonly used medications to lower cholesterol and prevent cardiovascular disease. Nearly half of patients stop taking statin medications one year after they are prescribed leading to higher cholesterol, increased cardiovascular risk, and costs due to excess hospitalizations. Identifying which patients are at highest risk for not adhering to long-term statin therapy is an important step towards individualizing interventions to improve adherence. Electronic health records (EHR) are an increasingly common source of data that are challenging to analyze but have potential for generating more accurate predictions of disease risk. The aim of this study was to build an EHR based model for statin adherence and link this model to biologic and clinical outcomes in patients receiving statin therapy. We gathered EHR data from the Military Health System which maintains administrative data for active duty, retirees, and dependents of the United States armed forces military that receive health care benefits. Data were gathered from patients prescribed their first statin prescription in 2005 and 2006. Baseline billing, laboratory, and pharmacy claims data were collected from the two years leading up to the first statin prescription and summarized using non-negative matrix factorization. Follow up statin prescription refill data was used to define the adherence outcome (> 80 percent days covered). The subsequent factors to emerge from this model were then used to build cross-validated, predictive models of 1) overall disease risk using coalescent regression and 2) statin adherence (using random forest regression). The predicted statin adherence for each patient was subsequently used to correlate with cholesterol lowering and hospitalizations for cardiovascular disease during the 5 year follow up period using Cox regression. The analytical dataset included 138 731 individuals and 1840 potential baseline predictors that were reduced to 30 independent EHR “factors”. A random forest predictive model taking patient, statin prescription, predicted disease risk, and the EHR factors as potential inputs produced a cross-validated c-statistic of 0.736 for classifying statin non-adherence. The addition of the first refill to the model increased the c-statistic to 0.81. The predicted statin adherence was independently associated with greater cholesterol lowering (correlation = 0.14, p < 1e-20) and lower hospitalization for myocardial infarction, coronary artery disease, and stroke (hazard ratio = 0.84, p = 1.87E-06). Electronic health records data can be used to build a predictive model of statin adherence that also correlates with statins’ cardiovascular benefits.</p></div

Baseline patient and statin characteristics of cohort (N = 138731).

<p>Baseline patient and statin characteristics of cohort (N = 138731).</p

Independent association between statin adherence model and cardiovascular disease hospitalization.

<p>Independent association between statin adherence model and cardiovascular disease hospitalization.</p

Predicted statin adherence and risk of cardiovascular outcomes.

<p>Predicted statin adherence was divided into tertiles of predicted statin adherence. The cumulative event free survival for each tertile of risk from Cox survival model is plotted for hospitalizations for acute myocardial infarction, stroke, coronary artery disease, or a composite of all three. P-values represent results of log-rank testing.</p

Groups of electronic health record codes and their association with higher or lower statin adherence.

<p>Groups of electronic health record codes and their association with higher or lower statin adherence.</p

Performance of statin adherence models.

<p>The Receiver operating characteristics (ROC) curves for two models that predict statin adherence defined as percent days covered (PDC) greater than 0.8 during the follow-up period. The results of the risk only model uses random forest modeling and considers baseline demographics, statin prescription characteristics, disease risk predictions, and the ‘factors” resulting from dimension reduction to predict statin adherence. The “risk + first refill” model uses the same predictors as the risk only model but also considers whether or not the first statin prescription was filled and predicts statin adherence for the remaining time period after the first fill. The area represents the area under the ROC curve.</p

Module-Based Association Analysis for Omics Data with Network Structure

<div><p>Module-based analysis (MBA) aims to evaluate the effect of a group of biological elements sharing common features, such as SNPs in the same gene or metabolites in the same pathways, and has become an attractive alternative to traditional single bio-element approaches. Because bio-elements regulate and interact with each other as part of network, incorporating network structure information can more precisely model the biological effects, enhance the ability to detect true associations, and facilitate our understanding of the underlying biological mechanisms. How-ever, most MBA methods ignore the network structure information, which depicts the interaction and regulation relationship among basic functional units in biology system. We construct the con-nectivity kernel and the topology kernel to capture the relationship among bio-elements in a mod-ule, and use a kernel machine framework to evaluate the joint effect of bio-elements. Our proposed kernel machine approach directly incorporates network structure so to enhance the study effi-ciency; it can assess interactions among modules, account covariates, and is computational effi-cient. Through simulation studies and real data application, we demonstrate that the proposed network-based methods can have markedly better power than the approaches ignoring network information under a range of scenarios.</p></div